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Cataloged from prism-ml/Bonsai-1.7B-gguf
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Prism ML Website | Whitepaper | Demo & Examples | Colab Notebook | Discord
End-to-end 1-bit language model for llama.cpp (CUDA, Metal, CPU)
13.9x smaller than FP16 | 3.0x faster on RTX 4090 | runs on any device
Each weight is a single bit: 0 maps to −scale, 1 maps to +scale. Every group of 128 weights shares one FP16 scale factor.
Effective bits per weight: 1.125 (1 sign bit + 16-bit scale amortized over 128 weights).
Parameter memory only (weights and scales loaded into memory):
The GGUF file on disk is 0.25 GB (~6.2 MB larger) because the format embeds the tokenizer, chat template, and model metadata alongside the weights.
You can use a simple system prompt such as:
Open the web UI at http://127.0.0.1:8080, or see our llama.cpp fork for more examples.
If you use 1-bit Bonsai 1.7B, please cite:
For questions, feedback, or collaboration inquiries: contact@prismml.com
You are a helpful assistant# Clone the PrismML fork of llama.cpp (includes Q1_0 kernels)git clone https://github.com/PrismML-Eng/llama.cppcd llama.cpp
# Build with CUDA supportcmake -B build -DGGML_CUDA=ON && cmake --build build -j
# Run inference./build/bin/llama-cli \ -m Bonsai-1.7B-Q1_0.gguf \ -p "Explain quantum computing in simple terms." \ -n 256 \ --temp 0.5 \ --top-p 0.85 \ --top-k 20 \ -ngl 99# Clone the PrismML fork of llama.cpp (includes Q1_0 kernels)git clone https://github.com/PrismML-Eng/llama.cppcd llama.cpp
# Build with Metal support (default on macOS)cmake -B build && cmake --build build -j
# Run inference./build/bin/llama-cli \ -m Bonsai-1.7B-Q1_0.gguf \ -p "Explain quantum computing in simple terms." \ -n 256 \ --temp 0.5 \ --top-p 0.85 \ --top-k 20 \ -ngl 99./build/bin/llama-server \ -m Bonsai-1.7B-Q1_0.gguf \ --host 0.0.0.0 \ --port 8080 \ -ngl 99@techreport{bonsai, title = {Bonsai: End-to-End 1-bit Language Model Deployment Across Apple, GPU, and Mobile Runtimes}, author = {Prism ML}, year = {2026}, month = {March}, url = {https://prismml.com}}